Certificate Course in R Programming

Mode: Classroom

Duration: 30 hours

Pre-requisite: None but knowledge of statistics helps

Course Outline

Introduction

• Getting R and Getting Started
• Getting and Using R
• A First R Session
• Moving around in R
• Working with Data in R
• Dealing with Missing Data in R

Programming in R

• What is Programming?
• Getting Ready to Program
• The Requirements for Learning to Program
• Flow Control
• Essentials of R Programming
• Understanding the R Environment
• Implementation of Program Flow in R
• A First R Program
• Example - finding Pythagorean Triples
• Using R to Solve Quadratic Equations
• Why R is Object-Oriented

Writing Reusable Functions

• Examining R Function from Base-R Code
• Creating a Function
• Calculating a Confidence Interval for a

Mean

• Avoiding Loops with Vectorized Operations
• Vectorizing If-Else Statement Using ifelse()
• Making More Powerful Functions
• Any, All, and Which
• Making Functions More Useful
• Confidence Intervals Revisited

Summary Statistics

• Measuring Central Tendency
• Measuring Location via Standard Scores
• Measuring Variability
• Covariance and Correlation
• Measuring Symmetry (or Lack Thereof)

Creating Tables and Graphs

• Frequency Distributions and Tables
• Pie Charts and Bar Charts
• Boxplots
• Histograms
• Line Graphs
• Scatterplots
• Saving and Using Graphics

Discrete Probability Distributions

• Discrete Probability Distributions
• Bernoulli Processes
• Relating Discrete Probability to Normal Probability

Computing Normal Probabilities

• Characteristics of the Normal Distribution
• The Sampling Distribution of Means
• A One-Sample z Test

Creating Confidence Intervals

• Confidence Intervals for Means
• Confidence Intervals for Proportions
• Understanding the Chi-Square Distribution
• Confidence Intervals for Variances and Standard Deviations
• Confidence Intervals for Differences between Means
• Confidence Intervals Using the stats Package

Performing t Tests

• A Brief Introduction to Hypothesis Testing
• Understanding the t Distribution
• The One-Sample t Test
• The Paired-Samples t Test
• Two-Sample t Tests
• A Note on Effect Size for the t Test

One-Way Analysis of Variance

• Understanding the F Distribution
• Using the F Distribution to Test Variances
• Compounding Alpha, Post Hoc Comparisons
• One-Way ANOVA
• Using the anova Function

Advanced Analysis of Variance

• Two-Way ANOVA
• Repeated-Measures ANOVA
• Mixed-Factorial ANOVA

Correlation and Regression

• Covariance and Correlation
• Regression
• An Example: Predicting the Price of Gasoline
• Determining Confidence and Prediction Intervals

Multiple Regression

• The Multiple Regression Equation
• Multiple Regression Example: Predicting Job Satisfaction
• Using Matrix Algebra to Solve a Regression Equation
• Brief Introduction to the General Linear Model
• More on Multiple Regression

Logistic Regression

• What is Logistic Regression?
• Logistic Regression with One Dichotomous

Predictor

• Logistic Regression with One Continuous

Predictor

• Logistic Regression with Multiple Predictors
• Comparing Logistic & Multiple Regression
• Alternatives to Logistic Regression

Chi-Square Tests

• Chi-Square Tests of Goodness of Fit
• Chi-Square Tests of Independence
• A Special Case: Two-by-Two Contingency

Tables

• Relating the Standard Normal Distribution

to Chi-Square

• Effect Size for Chi-Square Tests
• Demonstrating the Relationship of Phi to the Correlation Coefficient

Nonparametric Tests

• Nonparametric Alternatives to t Tests
• Nonparametric Alternatives to ANOVA
• Nonparametric Alternatives to Correlation

Using R for Simulation

• Defining Statistical Simulation
• Some Simulations in R

The "New" Statistics—Resampling and Bootstrapping

• The Pitfalls of Hypothesis Testing
• The Bootstrap
• Jackknifing
• Permutation Tests
• More on Modern Robust StatisticalMethods

Making an R Package

• The Concept of a Package
• Some Windows Considerations
• Establishing the Skeleton of an R Package
• Editing the R Documentation
• Building and Checking the Package
• Installing the Package
• Making Sure the Package Works Correctly
• Maintaining Your R Package

The R Commander Package

• The R Commander Interface
• Examples of Using R Commander for Data Analysis